Additional file 1 of Graphical calibration curves and the integrated calibration index (ICI) for competing risk models
Bibliographic record
Abstract
Additional file 1. Figure S1. RCS: Choice of number of knots (p = 0.25). Figure S2. RCS: Choice of number of knots (p = 0.75). Figure S3. ICI/E50/E90 in simulations for selecting the optimal number of knots (p = 0.25). Figure S4. ICI/E50/E90 in simulations for selecting the optimal number of knots (p = 0.75). Figure S5. Effect of degree of censoring on estimated calibration curves (N = 2000 and p = 0.25). Figure S6. Effect of degree of censoring on estimated calibration curves (N = 2000 and p = 0.75). Figure S7. ICI/E90/E90 for correctly−specified model and censoring (N = 2000 and p = 0.25). Figure S8. ICI/E90/E90 for correctly−specified model and censoring (N = 2000 and p = 0.75). Figure S9. True model fitted with no censoring (beta1 = 0.25 & p = 0.25). Figure S10. True model fitted with no censoring (beta1 = 0.25 & p = 0.50). Figure S11. True model fitted with no censoring (beta1 = 0.25 & p = 0.75). Figure S12. True model fitted with no censoring (beta1 = 0.50 & p = 0.25). Figure S13. True model fitted with no censoring (beta1 = 0.50 & p = 0.75). Figure S14. True model fitted with no censoring (beta1 = 1 & p = 0.25). Figure S15. True model fitted with no censoring (beta1 = 1 & p = 0.50). Figure S16. True model fitted with no censoring (beta1 = 1 & p = 0.75). Figure S17. ICI/E90/E90 for correctly−specified model without censoring (p = 0.25). Figure S18. ICI/E90/E90 for correctly−specified model without censoring (p = 0.75). Figure S19. Mis−specified model (beta1 = 0.25 & p = 0.25). Figure S20. Mis−specified model (beta1 = 0.25 & p = 0.50). Figure S21. Mis−specified model (beta1 = 0.25 & p = 0.75). Figure S22. Mis−specified model (beta1 = 0.50 & p = 0.25). Figure S23. Mis−specified model (beta1 = 0.50 & p = 0.75). Figure S24. Mis−specified model (beta1 = 1 & p = 0.25). Figure S25. Mis−specified model (beta1 = 1 & p = 0.50). Figure S26. Mis−specified model (beta1 = 1 & p = 0.75). Figure S27. ICI/E90/E90 for incorrectly−specified model (p = 0.25). Figure S28. ICI/E90/E90 for incorrectly−specified model (p = 0.75). Figure S29. Mis−specified model (omission of main effect) (rho=0.25). Figure S30. Mis−specified model (omission of main effect) (rho=0.50). Figure S31. Mis−specified model (omission of main effect) (rho=0.75). :R code for constructing calibration curves and numerical metrics of calibration using restricted cubic splines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.896 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".